NREL+用于能源系统研究的大型语言模型-英-17页_822kb

LLMs for Energy Systems Research Workshop Summary

The presentation discusses the application of Large Language Models (LLMs) in disseminating and analyzing research for energy systems, particularly focusing on renewable energy studies from NREL (National Renewable Energy Laboratory). Presented by Grant Buster on November 1, 2023, it highlights how NREL produces vast amounts of text and addresses effective dissemination methods.

LLMs are used to answer queries and summarize research by processing and retrieving information from documents. Examples include responses to questions about the Puerto Rico 100% renewable energy study and hypothetical scenarios, demonstrating LLM capabilities over outdated AI models.

The technical approach involves converting text into vector embeddings and chunked data for efficient retrieval, as seen in query-based responses. This method is applied to parse legal documents like wind and solar siting ordinances, with accuracies ranging from 82% for ordinance databases to 90% when combining LLMs with decision trees.

Complex rule handling is shown in cases like Monroe County, Wisconsin, segregation rules, where LLMs guided by decision trees improve accuracy in tasks like calculating setbacks for wind energy systems.

Overall, LLMs enhance research dissemination and data processing with tools like ELM, an open-source platform, achieving high efficiency but relying on updated legal data.